In the analysis of time series, it is frequent to classify perturbations as Additive Outliers (AO), Innovative Outliers (IO), Level Shift (LS) outliers or Transitory Change (TC) outliers. In this paper, a new outlier type, the Seasonal Level Shift (SLS), is introduced in order to complete the...

The Seasonal Adjustment Research Appraisal committee was created in Italy to evaluate procedures for seasonal adjustment of economic series. Because the TRAMO-SEATS programs were one of the main procedures considered, the committee sent a selection of 11 series of interest to be analysed. This...

The paper contains some implications for applied econometric research. Two important ones are, first, that invertible models, such as AR or VAR models, cannot in general be used to model seasonally adjusted or detrended data. The second one is that to look at the business cycle in detrended...

The paper deals with seasonal adjustment and trend estimation as a signal extraction problem in a regression-ARIMA model-based framework. This framework includes the capacity to preadjust the series by removing outliers and deterministic effects in general. For the preadjusted series the model...

The paper deals with estimation of missing observations in possibly nonstationary ARIMA models. First, the model is assumed known, and the structure of the interpolation filter is analysed. Using the inverse or dual autocorrelation function it is seen how estimation of a missing observation is...

The present document details, step by step, an efficient and simple way to construct the file input for the programs TRAMO ("Time Series Regression with ARIMA Noise Missing Observations, and Outliers") and SEATS ("Signal Extraction in ARIMA Time Series") for all possible cases and applications....